It was first made public in late 2015, while the first stable version appeared in 2017. Once developed and scaled, it allows lots of people to use it. They build a framework called Tensorflow to let researchers and developers work together on an AI model. Google saw it could use these deep neural networks to improve its services: History of TensorFlowĪ couple of years ago, deep learning started to outperform all other machine learning algorithms when giving a massive amount of data. It was built to run on multiple CPUs or GPUs and even mobile operating systems, and it has several wrappers in several languages like Python, C++ or Java. TensorFlow is a library developed by the Google Brain Team to accelerate machine learning and deep neural network research. Google does not just have any data they have the world’s most massive computer, so Tensor Flow was built to scale. They can all use the same toolset to collaborate with each other and improve their efficiency. Three different groups use machine learning: Google wants to use machine learning to take advantage of their massive datasets to give users the best experience. If the user types a keyword in the search bar, Google provides a recommendation about what could be the next word. To give a concrete example, Google users can experience a faster and more refined search experience with AI. In this TensorFlow tutorial, you will learn: Google product uses machine learning in all of its products to improve the search engine, translation, image captioning or recommendations. It allows developers to create machine learning applications using various tools, libraries, and community resources.Ĭurrently, the most famous deep learning library in the world is Google’s TensorFlow. It is a symbolic math library that uses dataflow and differentiable programming to perform various tasks focused on training and inference of deep neural networks.
TensorFlow is an open-source end-to-end platform for creating Machine Learning applications.
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